Exome sequencing and large-scale analysis of electronic medical record-linked biobank data identify candidate deafness genes.

Authors

Zippora Brownstein, Lara Kamal, Yazeed Zoabi, Keren Gesin, Hadas Knoller, Shadi Khoury, Roni Hahn, Sapir Brand, Daphna Marom, Hagit Shani, Nada Danial-Farran, Stavit Shalev, Varda Shalev, Asaf Peretz, Tal Patalon, Noam Shomron, Karen B Avraham

Published in

Journal of medical genetics. Sep 03, 2026. Epub Sep 03, 2026.

Abstract

Rapid advances in whole-exome sequencing (WES) have enabled large-scale detection of pathogenic variants. Although hundreds of genes are implicated in hearing loss, up to half of inherited cases remain unsolved, limiting eligibility for gene therapy trials that require genetic diagnosis. Biobanks and electronic medical records (EMRs) offer opportunities to integrate genomic and clinical data at scale and expand the spectrum of hearing loss genes. Despite clinical value, EMRs often lack key information such as inheritance patterns, posing challenges for accurate interpretation.
WES was performed on DNA samples from 1038 hearing-impaired patients enrolled in the Maccabi Research and Innovation Center Tipa Biobank. Clinical data were extracted from EMRs. Audiograms were available for all cases, although data on age of onset, family history and mode of inheritance were mostly unavailable. We applied a scalable bioinformatics analysis strategy for high-throughput annotation, filtering and prioritisation of WES variants across more than 1000 patients, designed to accommodate incomplete and heterogeneous clinical records.
Using this approach, 15% of cases were solved or potentially solved through known or novel variants in established deafness genes. Homozygous variants in novel candidate genes were identified in 3% of cases. Functional characterisation was performed for promising candidate genes to validate their role in the ear.
These findings demonstrate that WES can determine disease aetiology in large, genetically heterogeneous populations, even in the context of incomplete clinical data. This approach supports large-scale genetic screening and provides a framework for identifying patients who may benefit from emerging gene-based therapies.

PMID:
42692809
Bibliographic data and abstract were imported from PubMed on 04 Sep 2026.

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